Gain insights into conducting meaningful data analysis in Python without prior programming experience. Discover Python fundamentals, data preprocessing, EDA, and predictive analytics for solving business problems.

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Python3

Ready to move past Excel for complex business analysis? Then you’ll find this course very helpful.

This hands-on introductory Data Science course is aimed at professionals and students who don't have any experience with programming. It will help you advance your career by preparing you to conduct meaningful data analysis in Python on any dataset — large or small.

You’ll begin with the fundamentals of Python, with focus on CSV files in Python, covering concepts like data preprocessing and Exploratory Data Analysis (EDA). In the second half, you'll focus on predictive and inferential analysis using statistical and machine learning techniques, and learn how these techniques can help solve business problems.

Data Science for Non-Programmers

# Correlation
**Correlation** is a mathematical technique that shows how strongly two variables are linked. It quantifies the strength of the relationship. For instance, we know that the weight and height of a person are correlated. Taller people tend to have more weight. Hence, we say that height and weight are correlated. 

Correlation is measured in terms of a number called **correlation coefficient**, which ranges from $-1$ to $1$. The value of $1$ or $-1$ denotes complete correlation, while $0$ indicates that no correlation is present between the two variables. Negative values mean there is an inverse relationship between the two variables, while a positive value denotes a direct relationship. 

Pandas has the function `corr` that can be called on a dataframe. Let's see an example of this on our [Default of Credit Card Clients Dataset](https://www.kaggle.com/uciml/default-of-credit-card-clients-dataset). 



This lesson will introduce how to calculate and visualize correlations between quantities in python.

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Correlation and Heatmaps

Correlation